Hallo,> fit12<-lmFit(qrg[,1:2]) > t12<-toptable(fit12,adjust="fdr",number=25,genelist=qrg$genes[,1]) > t12ID logFC t P.Value adj.P.Val B 522 PLAU_OP -6.836144 -8.420414 5.589416e-05 0.01212520 2.054965 1555 CD44_WIZ -6.569622 -8.227938 6.510169e-05 0.01212520 1.944046 Can anyone tell me what the difference is between P.Value and adj.P.Value? I need to analyse microarrays and should say if there exist differential expressed genes. Which P.Value should I use? Thanks, Corinna
Hi Corinna The p.adjusted value is the the p-value adjusted for Multiple Comparisons. Enter ?p.adjust to get more of an explanation. Regards JS --- -----Original Message----- From: r-help-bounces at r-project.org [mailto:r-help-bounces at r-project.org] On Behalf Of Schmitt, Corinna Sent: 11 February 2008 16:02 To: r-help at r-project.org Subject: [R] Difference between P.Value and adj.P.Value Hallo,> fit12<-lmFit(qrg[,1:2]) > t12<-toptable(fit12,adjust="fdr",number=25,genelist=qrg$genes[,1]) > t12ID logFC t P.Value adj.P.Val B 522 PLAU_OP -6.836144 -8.420414 5.589416e-05 0.01212520 2.054965 1555 CD44_WIZ -6.569622 -8.227938 6.510169e-05 0.01212520 1.944046 Can anyone tell me what the difference is between P.Value and adj.P.Value? I need to analyse microarrays and should say if there exist differential expressed genes. Which P.Value should I use? Thanks, Corinna ______________________________________________ R-help at r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Hallo,> fit12<-lmFit(qrg[,1:2]) > t12<-toptable(fit12,adjust="fdr",number=15000,genelist=qrg$genes[,1]) > t12ID logFC t P.Value adj.P.Val B 1560 orf6.2714 -5,95911144 -7,504537362 0,0000000000000616459272630 0,0000000000430961073320568 20,85141454 8689 SW23 2,709344216 3,41198098 0,0006449261297639210000000 0,0396758555030764000000000 -0,62704052 The data example comes from one experiment, where I want to know if genes are differentially expressed. As I saw in the onlinehelp for toptable the value B is the log odds that the gene is differentially expressed. When I now look at the B value 20,85141454 it says that the gene orf6.2714 is in 20,85% differentially expressed. Is it right? But how should I interpret the second example SW23 with a negative B value? Can anyone discribe it to me in easy word? ;-) Thanks, Corinna [[alternative HTML version deleted]]
Dear Corinna, please do post questions related to bioconductor packages directly to the bioconductor mailing list. You will have a much higher chance to get a helpful answer. The B-statistic is explained best explained in the limma user guide (chapter 10), which comes with the limma package or from http://www.bioconductor.org/packages/release/bioc/html/limma.html Hope that helps, Georg "Schmitt, Corinna" <Corinna.Schmitt at igb.fraunhofer.de> writes:> Hallo, > >> fit12<-lmFit(qrg[,1:2]) >> t12<-toptable(fit12,adjust="fdr",number=15000,genelist=qrg$genes[,1]) >> t12 > ID logFC t P.Value adj.P.Val B > 1560 orf6.2714 -5,95911144 -7,504537362 0,0000000000000616459272630 0,0000000000430961073320568 20,85141454 > 8689 SW23 2,709344216 3,41198098 0,0006449261297639210000000 0,0396758555030764000000000 -0,62704052 > > > The data example comes from one experiment, where I want to know if genes are differentially expressed. As I saw in the onlinehelp for toptable the value B is the log odds that the gene is differentially expressed. When I now look at the B value 20,85141454 it says that the gene orf6.2714 is in 20,85% differentially expressed. Is it right? But how should I interpret the second example SW23 with a negative B value? >